ML Engineer - Coding Agent Expert - AI Trainer

mercor; United States; Remote; USD 99,000 - 135,000; Full time

Job summary

Mercor is recruiting an ML Engineer (Coding Agent Experience) for contract work. The role focuses on leveraging frontier AI coding agents to execute complex ML tasks remotely in the United States.

You will review model-generated implementations across training, inference, MLOps, and LLM applications, identifying bugs, edge cases, and performance issues while evaluating trade-offs among different models.

Qualifications

• 2+ years of professional machine learning engineering experience.
• Experience building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated ML implementations and technical tradeoffs.

Responsibilities

• Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
• Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and failure modes in AI models.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic ML engineering scenarios.

Skills

ML engineering

Production ML systems

AI coding agents

Model evaluation

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

About the job Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey . Position: ML Engineer (Coding Agent Experience) Type: Contract Compensation: $85/hour Location: Remote

Role Responsibilities

• Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks.
• Review model-generated implementations involving model training , inference systems , MLOps , and LLM applications .
• Identify bugs, edge cases, performance issues, and failure modes in AI models .
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic ML engineering scenarios.

Qualifications Must-Have

• 2+ years of professional machine learning engineering experience.
• Experience building production ML systems , model deployment infrastructure, LLM applications , or AI-powered products .
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated machine learning implementations and technical tradeoffs.

Preferred

• Experience deploying ML systems to production.

Compensation & Legal

• $400 per accepted task
• Compensation is tied to accepted work.